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A Brain Adaptation View of Plasticity: Is Synaptic Plasticity An Overly Limited Concept? There is a long tradition, traceable to the early musings of Ramon y Cajal, of focusing upon the neuron as the only plastic cell type of any importance within the brain, and upon the synapse as the only important plastic aspect regulating the interactions among neurons. While neuronal plasticity is without question an important aspect of brain function, it has become increasingly clear that other cellular elements of brain are plastic and that their plasticity can contribute to brain function. Moreover, it is becoming clear in work of others that there are multiple forms of synaptic plasticity: the synaptic number response to a complex environment, for example, occurs in animals genetically rendered incapable of the most common form of LTP. Our work and that of others indicates that oligodendrocytes, astrocytes, vasculature, and perhaps other cellular elements exhibit plasticity quantitatively equivalent to that of neurons in the developing and mature brain and that modifications of these cellular elements may be brought about by experience. It also suggests that multiple forms of plasticity may occur at the synapse. In short, while memory researchers largely focus on naturallyand artificially-induced changes in synaptic connectivity, the brains of real animals (and presumably people) in real-world situations are in a dynamic state in which synaptic adjustment may in some cases be a relatively small part of the mix. The intent of this review is to consider the data that point to this view and consider how we might assess nonsynaptic effects of learning and/or determine whether the effects of learning upon measures of brain functional organization may already be affected by these nonsynaptic changes. Since the early speculations of Tanzi and Ramon y Cajal, the synapse has been the principal proposed site of plasticity underlying learning and memory in the brain. Tanzi (1893) initially emphasized the possibility of strength changes in pre-existing connections while Ramon y Cajal (1893) stressed the formation and loss of connections. The ability to examine these possibilities depended on development of adequate tools, but, by the early 1970s, electrophysiological and anatomical evidence for the ability of the nervous system to alter its functional connectivity in accord with its experience was becoming reasonably well established. Electrophysiologically, long-term potentiation had been described (Bliss and Lomo, 1973). Anatomically, there was evidence that synapses in the late developing and adult nervous system could both form and change in size in response to experience; functional innervation of neurons by surviving axons occurred spontaneously in response to denervation (e.g., Lynch et al., 1973; Raisman and Field, 1973 ); exposure to a complex environment from weaning through adolescence increased dendritic field dimensions (Volkmar and Greenough, 1972) and synaptic size (West and Greenough, 1972) in the rat visual cortex; and parallel findings were described in invertebrates, where there was more precise knowledge of the underlying structure-function relationship (e.g., Bailey and Chen, 1988). This paper summarizes progress in understanding the brain plasticities thought to be associated with learning and memory, focusing heavily on our own work, since those relatively early beginnings. A relatively early specific demonstration that synapses formed in response to experience was the report by Turner and Greenough (1985) that there were more synapses per neuron in upper layers of the visual cortex in rats that had been reared from weaning in a complex environment. This rearing and adult housing paradigm, pioneered by Hebb (1949) and his students (e.g. Hymovitch, 19xx Jay has this one) using behavioral measures, and first used as a tool for exploring brain plasticity by Krech, Rosenzweig, Bennett and colleagues (e.g., Bennett et al., 1964), has been used extensively to examine the range of plasticity of various elements of brain other than neurons, but most of these results have failed to become incorporated into the continuing literature on brain plasticity. Early in this history, Diamond (Diamond et al., 1966) reported that glial cells in visual cortex exhibited morphological changes in response to experience in parallel with the changes reported in neuronal dendrites and synapses. We have subsequently examined plasticity of non-neuronal elements of the cerebral cortex in response to complex environment exposure in some detail. These results are addressed at a later point in this chapter. Following up on these initial demonstrations of dendritic and synaptic responsiveness to rearing conditions, neuronal responsiveness to neuronal activity and experience has subsequently been demonstrated in a wide variety of brain structures: hippocampus ( Moser et al., 1994;), basal ganglia (Comery et al., 1995; Comery et al., 1996), cerebellar cortex (Greenough et al., 1986) —someone to fill this in with both our data and others.** At this point, neuronal plasticity seems to be the rule rather than the exception in CNS structures, although there are some striking failures to demonstrate it in some cases ((Kleim et al., 1998; but see [Jeff's new paper]) [Jeff can add more here or elsewhere] Similarly, these forms of neuronal plasticity occur relatively consistently across the age spectrum and do not appear to be restricted to particular critical or sensitive periods of development. Effects of adult exposure to a complex environment have been shown to affect dendritic field dimensions in the visual cortex of adult, middle-aged and even elderly rats (e.g., (Juraska et al., 1980); (Green et al., 1983); Black et al., 1986; Conner et al., 1981). Synapse numbers per neuron are increased in adults placed in complex environments (Briones et al., emailed as in prep). Likewise, cerebellar plasticity, while not thoroughly studied, seems to occur throughout the lifespan, although this may occur in the context of a decline in elderly animals (e.g., (Floeter and Greenough, 1979); Pysh & Weiss, 1979; (Greenough et al., 1986)). It should be briefly noted that there is a reason that the oft-used term “enriched” is avoided here. These laboratory environments are simply not enriched relative to the norm of wild or feral rats. We would argue that no one has published studies of the brains of rats provided levels of environmental complexity and challenge beyond the level provided by the natural environment, and studies of wild animals have for years confirmed that feral animal brains are larger than those of domestically reared comparable animals (old german and other literature, just need one ref can’t find). Nonetheless, studying different degrees of environmental complexity can provide information about brain responses that are likely to generalize to higher levels of stimulation, as suggested also by studies of human development (e.g., Hart and Risley, 1995). Non-neural forms of brain plasticity This neuronal/synaptic plasticity is accompanied by plasticity of non-neural elements such as glia and vasculature. Subsequent work by Juraska and Kopcik (1988) indicated enhanced myelination of of splenial axons in EC rats. Further work has suggested that all type of brain tissue may exhibit plasticity, at least in regions such as the cerebral and cerebellar cortices where this has been examined. Perhaps surprisingly, both in terms of earlier reports (e.g., Diamond ??64 (Rowan and Maxwell, 1981) See Black et al 1987 reprint) and in terms of long held beliefs regarding vascular development ( e.g., Bar, 1980), the capillary system exhibits two forms of plasticity: Capillaries are both larger, on average, and more frequent, it terms of measures such as the fractional volume of capillaries per neuron, as shown in Fig. 1 (as we discussed). In fact, on a percentage change per neuron basis, capillaries exhibit far more plasticity than synapses, with an increase of over 80% in volume fraction per neuron in rats placed in complex environments at weaning (Black et al., 1987), (Sirevaag et al., 1988); As we noted, check % and left panel of graph). While the greatest capillary plasticity is seen in weanlings, this plasticity continues into adulthood, diminishing with age (Black et al., 1989). Similarly, astrocytic plasticity evident in measures of nuclei (?(Diamond et al., 1966); Sirevaag and Greenough, 1987) reflects plasticity of astrocytic branching. The extent of GFAP-immunoreactive astrocytic processes was greater in EC than in IC rats (Sirevaag and Greenough, 1991). These effects of complexity that occur in brain areas that exhibit synaptic plasticity are differentiable from effects of stress seen, e.g., in the hippocampal formation (Sirevaag et al., 1991) and, at least in cerebellar cortex, parallel plasticity of synapse number (Anderson et al., 1994, see below); see also (Sirevaag and Greenough, 1988). At a higher level of resolution, astrocytic process proliferation is more specifically associated with synapses, which are more completely ensheathed by astrocytic processes in EC rats (Jones and Greenough, 1996), presumably reflecting the function of optimizing the synaptic microenvironment. Myelination continues well into human adulthood ((Yakovlev and Lecours, 1967), (Benes et al., 1994)), and the first evidence for experience effects on oligodendrocyte development came from Szeligo and Leblond (Szeligo and Leblond, 1977). Subsequently Juraska and Kopcik found that EC rats had more myelinated axons than IC rats in the splenial corpus callosum. A recent finding (Briones et al., 1999) indicates that experience continues to have dramatic effects on the adult myelination process in the splenial corpus callosum. Particularly interesting is that these effects of experience on myelination, as with those for the addition of synapses (e.g., (Camel et al., 1986; see also Kleim et al., 1997a below); Briones, synapse adult in preparation ) exhibit a “ratchet” effect: unlike experience induced changes in glia which appear to fade rapidly once differential experience is discontinued (e.g., Kleim SFN abstract—paper still to be written IN REVISION), changes in myleination appear to be stable across a subsequent 30 day period of return to an individual cage housing condition (Briones SFN abstract; Fig. X). It is interesting to speculate that added synapses and myelin are stable because they represent permanent, survival-important additions to the “wiring diagram” of the brain whereas astrocytic and vascular (yet to be tested) changes are adjustments to immediate demands of experience that can be reversed, saving metabolic investment, in the absence of continued environmental pressure. The overriding message, in any case, is that the brain is the organ of adaptation—-the interface between the individual and its environment. As such it dynamically adjusts to the past demands placed upon it by experience, as if assuming that the experiences of the past must be good predictors of the future. What drives the plasticity of brain tissues? The existence of short-term and long-term processes of brain cellular adaptation, and the fact that physical activity and learning are both involved in the behavioral events that appear to drive these processes, leads to the next natural question: what causes changes in neurons, glia and vasculature. At the topmost level (as opposed, say, to the level of trophic factors or receptor subunits), we can propose two general causal forces at the behavioral level: the general categories of activity and learning. With regard to activity, we have muscle as a model: with sufficient activity, muscle will hypertrophy, the particular details varying with the extent and pattern of activation. One can suppose that activation of brain tissues in association with peripheral activity, via intermediary cellular events, might similarly trigger neuronal, glial or vascular hypertrophy of the sort seen in rats after complex environment housing. With regard to learning, it seems plausible that certain changes in neurons, astrocytes or oligodendrocytes might be learning-specific, although it is harder to imagine that changes in capillaries would play very specific roles in learning. At a broader level, it is also possible to imagine that responses to training such as stress, or related metabolic consequences of behavioral manipulations could lead to changes in brain tissue. Certainly stress can have negative consequences for at least some brain regions (e.g., (Sapolsky, 1996)), although the adrenal hypertrophy-correlated astroglial changes in the hippocampal formation appear to be dissociated from the experience-correlated visual cortex changes in complex environment research (Sirevaag et al., 1991). To examine the roles of learning vs. other consequences of training on neuronal changes, we have utilized paradigms in which the consequences of learning would be focused in particular regions in the brain for which other regions could serve as control or comparison samples. In one early study, Chang and Greenough (1982) compared rats trained in a complex series of changing maze patterns that learned with either the same eye always occluded or with occlusion of alternate eyes on successive days. Both groups had been previously subjected to transection of the corpus callosum, a “split-brain” procedure that disrupts communication between the two hemispheres of the brain, such that the unilaterally-trained rats should have most training input restricted to the hemisphere opposite the open eye, whereas the bilaterallyalternating training should have allocated the learning input about equally to both hemispheres. Controls that were surgically operated and subsequently handled but not trained were divided into unilaterally and bilaterally occluded groups. The result indicated increased dendritic branching, a correlate of increased synapse number, in both hemispheres of the alternately trained group relative to the nontrained group and in the non-occluded hemisphere of the unilaterally trained group, compared to the occluded hemisphere. This result indicates that either training or training-related activity drives dendritic plasticity. A similar study used unilateral vs. bilateral training to reach with the forelimb into a chamber for food with similar untrained controls, but without surgical intervention. The results in this somatosensory-somatomotor study were similar: for deep pyramidal neurons of the type that control forelimb activity, dendritic branching was greater opposite trained forelimbs (Greenough et al., 1985); for more superficial pyramidal neurons, there were effects of training, but these effects were not restricted to the “trained” side in unilaterallytrained animals (Withers and Greenough, 1989). Taken with the study above, the results indicate that learning or some other aspect of training-related activity drives morphological change in neurons. Both experiments make clear that a generally-acting hormonal or metabolic effect would be expected to alter comparable regions of the brain whether or not they were bein An obvious issue remaining is that with which we opened this section: whether activity or learning causes structural changes in the brain. To address this directly, Black et al. (1990) created adult female rat groups that had 1) a substantial amount of learning with relatively little physical activity (AC below), 2) a substantial amount of physical activity with relatively little learning (FX and VX below) or 3) minimal opportunity for physical activity or learning (IC below). ACrobatic rats (AC) completed a multi-element elevated obstacle course that required learning significant motor skill while providing only limited exercise. Forced eXercise (FX) rats ran on a treadmill, reaching durations of 60 minutes a day, exercising but with very little learning. Voluntary eXercise (VX) rats had access to running wheels attached to their cages and were the only group to exhibit increased heart weight, a sign of aerobic exercise. Inactive Condition (IC) rats were merely removed from their cages for brief daily experimenter handling, providing neither activity or learning. Results were clear in initial studies focusing on cerebellar cortex. As Fig. XA shows, when blood vessel density was measured, the FX and VX groups both had more than the AC or IC groups, which did not differ; this suggests that the formation of new capillaries was driven by neural activity. By contrast, when the number of synapses per neuron was measured, shown in Fig. XB, the learning group, AC, exceeded the other 3 groups, which did not differ, suggesting that when learning takes place, new synapses are formed. There is one other interesting thing about these synapses—many of them involve additional postsynaptic spines contacting presynaptic varicosities on which one or more spines already exist (Federmeier et al., submitted). A similar result has recently been reported by [Geinisman/Disterhoft](Geinisman et al., 2001) in the ? cerebellar lobule see email following associative eyeblink conditioning. In general, according to (Kristen Harris), spines on a single cerebellar parallel fiber varicosity tend to arise from the same postsynaptic cell, such that they are separate parallel lines between a parallel fiber and a Purkinje (or stellate) neuron NEW NATURE NS PAPER ENTITLED “DENDRITIC SPINES DO NOT SPLIT DURING HIPPOCAMPAL LTP OR MATURATION” BY HARRIS TO CONSIDER. The implications of this finding for wiring diagram level models of the learning process remain to be determined. It should be noted that this phenomenon is not unique to the cerebellum—we have seen multiple postsynaptic contacts increased on excitatory morphology presynaptic varicosities in the visual cortex of rats reared in complex environments in comparison to caged rats (Jones et al., 1997). [TA Jones et al] have also described increases in these multiple synapses in the intact cortex in the course of compensatory changes following unilateral cortical lesions, and a relatively early report indicated increased multiple postsynaptic innervation of presynaptic terminals associated with the open eye in monocularly-deprived [kittens?] (M. Friedlander). It should be noted that these effects are not limited to cerebellar cortex. Kleim et al. (papers and absts) have described synaptogenesis and changes in synapse morphology in association with the same AC motor learning procedure in the somatosensory-somatomotor forelimb cortex of rats. The first morphological change to occur is, on average, an increase in the size of PSDs, which occurs within one to two days after training begins. Subsequently, at the next day examined, day 5, an increase in the number of synapses per neuron was detected, and the average size of synapses decreased, possibly because the new synapses were, on average, smaller than the pre-existing synapse population. The increase in synapse number was maintained, drifting slowly, but not statistically, upward across the remainder of training. As training progressed, the average size of synapses again increased, possibly suggesting that the new synapses were growing larger or that the population of synapses overall was doing so. A schematic interpretation of these findings appears in Fig. X. There is a long history of evidence for involvement of synapse size changes in plasticity that cannot be reviewed here due to space limitations (is there a Harris or other review to which we could refer?) (Ask Jeff for input on this paragraph.) Need a paragraph on perforated synapses. Start with Greenough west devoogd, Science 1978 EC IC and developmental age findings. Include Geinisman LTP findings, other things included in Chapters that Chang & Greenough did for Cotman and Jones/Peters volumes (Greenough, W. T., & Chang, F.-L. F. Plasticity of synapse structure and pattern in the cerebral cortex. In E. G. Jones & A. Peters (Eds.), Cerebral Cortex. Vol. 7. New York: Plenum, 1988, pp. 391-440.) Include Jeff's stuff from Motor Cortex, any other recent work that supports this as a possible plasticity mechanism. We could think about interpretations. Are there other synapse modifications to be discussed. Given space constraints I suggest we merely mention vesicle aggregate size and some other possible measures and refer to review articles. Regulation of Astrocyte Plasticity Is this redundant? This was mentioned in the section on non-neuronal plasticity and perhaps should merely be elaborated more in that section There are two studies that might be discussed in more detail either in that section or here. One is Brenda's 1994? Paper showing the correlation between synapse number and astrocyte Vv. The other is Jeff's astrocyte persistence paper. Tj's ensheathment paper fits in this discussion. The point of putting it here is by way of a segue into a discussion of the tendency to ignore non neuronal (or even nonsynaptic) changes. This goes on the end or might be placed elsewhere: A note on Long-term potentiation Tell me if this seems out of place. At least 3 studies dissociate LTP from spatial behavior and morphological change. The primary point I want to make is the apparent dissociation of LTP from EC effects on synapses published by J. Tsien in Nature Neuroscience. This suggests that LTP and synaptogenesis are independent phenomena. However, Engert and Bonhoefferhave reported apparent synaptogenesis in vitro in association with LTP induction. I am not sure what the range of the evidence is or the weight of it (e.g., other more recent work that bears on this issue, most of which are likely to have cited both of the above studies and hence should be locatable via the science citation index, which I have not used for the last million years), but the dissociation to me seems most powerful-synapse addition may mediate LTP, but synapse addition need not involve an LTP-like process for its induction. If you want to take a crack at this, feel free. Otherwise I will. On the Horizon: A Role for Protein Synthesis at the synapse Since the first report of morphological evidence for protein synthesis at the synapse (Steward & Levy, 1992) there has been a growing literature investigating this phenomenon. Synaptic and dendritic protein synthesis have been shown to be activated by metabotropic glutamate receptors in some cases (e.g., Weiler & Greenough, 1993; weiler et al., 1994, 1997; eberwine PNAS-still in press?) and by NMDA receptors as well (Sheetz et al, 2000). Proteins synthesized at synapses ibnclude the fragile X protein FMRP and calcium/calmodulin-dependent protein kinase II (CAMKII). FMRP has also been shown to be necessary for the mGluR-dependent synthesis, which is not observed in FMR1 knockout mice (cite Spangler abstract). Plasticity-inducing forms of electrical stimulation have been shown to trigger the transcription and transport of mRNA for the protein ARC to dendritic sites of stimulation, where it is translated (Steward and Worley references). mGluR1 activation, ARC synthesis and CAMKII activity have been proposed to be involved in various forms of plasticity (Huber/Bear work; Steward; Mary Kennedy), although details of the specific functions of synaptic or dendritic protein synthesis are still under investigation. Do you think we need to say anything more here? The chapter is really not "about" this, and I am not sure (but open to suggestions) what additional data makes sense to include. Summary and Concerns: Synaptic Plasticity and Beyond The principal thing I want to add at this point is a summary that comes back to the main point of the chapter--that we are only looking at a small portion of what the brain does when it accomplishes plastic change. I really would like your feedback on the earlier stuff. I have attached a copy of the chapter file as it exists on my computer. There is a demarcated line below which all of my additions occur, so it should be possible to just paste what you have and the part that I added together. CHAPTER WORKING NOTES: Tissue cultures lacking astrocytes—how good a model? Lack of astro part of ECM. Lack of basis for TPA, other actions probably involved in synaptogenesis. MMP3, MMP6, MMP9 (Metalomatrix proteins), stromolysin, gelatinase. Roles of Astros, ECM, TPA, etc. in synaptogenesis; adhesions; rec aggregation Incorporate Harris, Matus, Segal. Motility and shape issues. Put together a model, slow accumulation of synapses via overproduction-selection as a basis for the stable long-term substrate of memory; plus fast shape changes, PSD size, perfs, interpret multiple synapses from local and wiring diagram view. 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